A quantitative aesthetic measurement method for product appearance design

Product appearance is one of the crucial factors that influence consumers’ purchase decisions. The attractiveness of product appearance is mainly determined by the inherent aesthetics of the design composition related to the arrangement of visual design elements. Hence, it is critical to study and improve the arrangement of visual design elements for product appearance design. Strategies that apply aesthetic design principles to assist designers in effectively arranging visual design elements are widely acknowledged in both academia and industry. However, applying aesthetic design principles relies heavily on the designer’s perception and experience, while it is rather challenging for novice designers. Meanwhile, it is hard to measure and quantify design aesthetics in designing artefacts when designers refer to existing successful designs. In this regard, this study aims to introduce a method that assists designers in applying aesthetic design principles to improve the attractiveness of product appearance. Furthermore, formulas for aesthetic measurement based on aesthetic design principles are also developed, and it makes an early attempt to provide quantified aesthetic measurements of design artefacts. A case study on camera design was conducted to demonstrate the merits of the proposed method where the improved strategies for the camera appearance design offer insights for concept generation in product appearance design based on aesthetic design principles

Research on the association mechanism and evaluation model between fNIRS data and aesthetic quality in product aesthetic quality evaluation

Aesthetic quality evaluation has been an important research question in the field of user experience in product design. However, the feasibility and accuracy of using fNIRS data for product aesthetic quality evaluation are unknown. In this paper, we analyze the correlation and association between fNIRS data and aesthetic quality and designed a product aesthetic quality evaluation model to answer this question. We find that HBO2 data in the prefrontal (S19-D11), frontal (S4-D3), temporal (S3-D1), and parietal (S8-D8) regions of the brain have significant correlations and logistic relationships with high visual product aesthetic quality, whereas HBO2 data in the prefrontal (S19-D11) and parietal (S8-D8) regions of the brain have significant correlations and association relationships. These data can be used for products aesthetic quality evaluation. Importantly, the overall prediction accuracy of the model to evaluate products’ aesthetic quality is 84.1%. The model is therefore able to better distinguish and evaluate the aesthetic quality of products. This study demonstrates the feasibility of using fNIRS data to evaluate the aesthetic quality of products and shows that the product aesthetic quality evaluation model can provide an objective and accurate decision-making reference to help designers evaluate and improve the aesthetic quality of products

A 3D shape generative method for aesthetic product design

[EN] This work describes a generative method for the exploration of product shapes in the conceptual design stage. The method is based on three concepts: the notion of grammars to capture product appearance, the implementation of sketching transformation rules to produce design variations and the use of a parametric modeller to build shapes. We represent product solutions as 3D sketches using combinations of basic shapes arranged in simple and schematic product structures. This procedure allows creating many varied configurations with a minimal number of shapes, and facilitates the adaptation of the generative model to different products. The performance of the method is demonstrated through several examples from the literature.Alcaide-Marzal, J.; Diego-Mas, JA.; Acosta-Zazueta, G. (2020). A 3D shape generative method for aesthetic product design. Design Studies. 66:144-176. https://doi.org/10.1016/j.destud.2019.11.003S14417666Agarwal, M., & Cagan, J. (1998). A Blend of Different Tastes: The Language of Coffeemakers. Environment and Planning B: Planning and Design, 25(2), 205-226. doi:10.1068/b250205Alcaide-Marzal, J., Diego-Más, J. A., Asensio-Cuesta, S., & Piqueras-Fiszman, B. (2013). An exploratory study on the use of digital sculpting in conceptual product design. Design Studies, 34(2), 264-284. doi:10.1016/j.destud.2012.09.001Aqeel, A. B. (2015). Development of Visual Aspect of Porsche Brand using CAD Technology. Procedia Technology, 20, 170-177. doi:10.1016/j.protcy.2015.07.028Bernal, M., Haymaker, J. R., & Eastman, C. (2015). On the role of computational support for designers in action. Design Studies, 41, 163-182. doi:10.1016/j.destud.2015.08.001Chang, Y.-S., Chien, Y.-H., Lin, H.-C., Chen, M. Y., & Hsieh, H.-H. (2016). Effects of 3D CAD applications on the design creativity of students with different representational abilities. Computers in Human Behavior, 65, 107-113. doi:10.1016/j.chb.2016.08.024Chase, S. C. (2002). A model for user interaction in grammar-based design systems. Automation in Construction, 11(2), 161-172. doi:10.1016/s0926-5805(00)00101-1Chase, S. C. (2005). Generative design tools for novice designers: Issues for selection. Automation in Construction, 14(6), 689-698. doi:10.1016/j.autcon.2004.12.004Chen, K., & Owen, C. L. (1998). A study of computer-supported formal design. Design Studies, 19(3), 331-359. doi:10.1016/s0142-694x(98)00002-7Cleveland, P. (2010). Style based automated graphic layouts. Design Studies, 31(1), 3-25. doi:10.1016/j.destud.2009.06.003Cluzel, F., Yannou, B., & Dihlmann, M. (2012). Using evolutionary design to interactively sketch car silhouettes and stimulate designer’s creativity. Engineering Applications of Artificial Intelligence, 25(7), 1413-1424. doi:10.1016/j.engappai.2012.02.011Company, P., Contero, M., Varley, P., Aleixos, N., & Naya, F. (2009). Computer-aided sketching as a tool to promote innovation in the new product development process. Computers in Industry, 60(8), 592-603. doi:10.1016/j.compind.2009.05.018Cook, M. T., & Agah, A. (2009). A survey of sketch-based 3-D modeling techniques. Interacting with Computers, 21(3), 201-211. doi:10.1016/j.intcom.2009.05.004Crilly, N. (2015). Fixation and creativity in concept development: The attitudes and practices of expert designers. Design Studies, 38, 54-91. doi:10.1016/j.destud.2015.01.002Crilly, N., & Cardoso, C. (2017). Where next for research on fixation, inspiration and creativity in design? Design Studies, 50, 1-38. doi:10.1016/j.destud.2017.02.001Cross, N. (1997). Descriptive models of creative design: application to an example. Design Studies, 18(4), 427-440. doi:10.1016/s0142-694x(97)00010-0Dang, M., Lienhard, S., Ceylan, D., Neubert, B., Wonka, P., & Pauly, M. (2015). Interactive design of probability density functions for shape grammars. ACM Transactions on Graphics, 34(6), 1-13. doi:10.1145/2816795.2818069Dekker, K. (1992). A future interface for computer-aided styling. Design Studies, 13(1), 42-53. doi:10.1016/0142-694x(92)80004-iVan Dijk, C. G. C. (1995). New insights in computer-aided conceptual design. Design Studies, 16(1), 62-80. doi:10.1016/0142-694x(95)90647-xVan Dijk, C. G. C., & Mayer, A. A. C. (1997). Sketch input for conceptual surface design. Computers in Industry, 34(1), 125-137. doi:10.1016/s0166-3615(96)00073-5Do, E. Y.-L., Gross, M. D., Neiman, B., & Zimring, C. (2000). Intentions in and relations among design drawings. Design Studies, 21(5), 483-503. doi:10.1016/s0142-694x(00)00020-xDorst, K., & Cross, N. (2001). Creativity in the design process: co-evolution of problem–solution. Design Studies, 22(5), 425-437. doi:10.1016/s0142-694x(01)00009-6Dou, R., Zong, C., & Nan, G. (2016). Multi-stage interactive genetic algorithm for collaborative product customization. Knowledge-Based Systems, 92, 43-54. doi:10.1016/j.knosys.2015.10.013Eloy, S., Pauwels, P., & Economou, A. (2018). AI EDAM special issue: advances in implemented shape grammars: solutions and applications. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 32(2), 131-137. doi:10.1017/s0890060417000634Gabriel, A., Monticolo, D., Camargo, M., & Bourgault, M. (2016). Creativity support systems: A systematic mapping study. Thinking Skills and Creativity, 21, 109-122. doi:10.1016/j.tsc.2016.05.009Gero, J. S. (1996). Creativity, emergence and evolution in design. Knowledge-Based Systems, 9(7), 435-448. doi:10.1016/s0950-7051(96)01054-4Gero, J. S. (2000). Computational Models of Innovative and Creative Design Processes. Technological Forecasting and Social Change, 64(2-3), 183-196. doi:10.1016/s0040-1625(99)00105-5Gael, A. K. (1997). Design, analogy, and creativity. IEEE Expert, 12(3), 62-70. doi:10.1109/64.590078Goldschmidt, G. (1991). The dialectics of sketching. Creativity Research Journal, 4(2), 123-143. doi:10.1080/10400419109534381Gong, D.-W., Hao, G.-S., Zhou, Y., & Sun, X.-Y. (2007). Interactive genetic algorithms with multi-population adaptive hierarchy and their application in fashion design. Applied Mathematics and Computation, 185(2), 1098-1108. doi:10.1016/j.amc.2006.07.043Granadeiro, V., Duarte, J. P., Correia, J. R., & Leal, V. M. S. (2013). Building envelope shape design in early stages of the design process: Integrating architectural design systems and energy simulation. Automation in Construction, 32, 196-209. doi:10.1016/j.autcon.2012.12.003Gunpinar, E., & Gunpinar, S. (2018). A shape sampling technique via particle tracing for CAD models. Graphical Models, 96, 11-29. doi:10.1016/j.gmod.2018.01.003Gu, Z., Xi Tang, M., & Frazer, J. H. (2006). Capturing aesthetic intention during interactive evolution. Computer-Aided Design, 38(3), 224-237. doi:10.1016/j.cad.2005.10.008Hewett, T. T. (2005). Informing the design of computer-based environments to support creativity. International Journal of Human-Computer Studies, 63(4-5), 383-409. doi:10.1016/j.ijhcs.2005.04.004Hoisl, F., & Shea, K. (2011). An interactive, visual approach to developing and applying parametric three-dimensional spatial grammars. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 25(4), 333-356. doi:10.1017/s0890060411000205Hsiao, S.-W., & Chen, C.-H. (1997). A semantic and shape grammar based approach for product design. Design Studies, 18(3), 275-296. doi:10.1016/s0142-694x(97)00037-9Hsiao, S.-W., Chiu, F.-Y., & Lu, S.-H. (2010). Product-form design model based on genetic algorithms. International Journal of Industrial Ergonomics, 40(3), 237-246. doi:10.1016/j.ergon.2010.01.009Hsiao, S.-W., & Tsai, H.-C. (2005). Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. International Journal of Industrial Ergonomics, 35(5), 411-428. doi:10.1016/j.ergon.2004.10.007Hsiao, S.-W., & Wang, H.-P. (1998). Applying the semantic transformation method to product form design. Design Studies, 19(3), 309-330. doi:10.1016/s0142-694x(98)00009-xHu, Z.-H., Ding, Y.-S., Zhang, W.-B., & Yan, Q. (2008). An interactive co-evolutionary CAD system for garment pattern design. Computer-Aided Design, 40(12), 1094-1104. doi:10.1016/j.cad.2008.10.010Hybs, I., & Gero, J. S. (1992). An evolutionary process model of design. Design Studies, 13(3), 273-290. doi:10.1016/0142-694x(92)90216-wHyun, K. H., & Lee, J.-H. (2018). Balancing homogeneity and heterogeneity in design exploration by synthesizing novel design alternatives based on genetic algorithm and strategic styling decision. Advanced Engineering Informatics, 38, 113-128. doi:10.1016/j.aei.2018.06.005Jansson, D. G., & Smith, S. M. (1991). Design fixation. Design Studies, 12(1), 3-11. doi:10.1016/0142-694x(91)90003-fKhan, S., & Awan, M. J. (2018). A generative design technique for exploring shape variations. Advanced Engineering Informatics, 38, 712-724. doi:10.1016/j.aei.2018.10.005Khan, S., & Gunpinar, E. (2018). Sampling CAD models via an extended teaching–learning-based optimization technique. Computer-Aided Design, 100, 52-67. doi:10.1016/j.cad.2018.03.003Kim, H.-S., & Cho, S.-B. (2000). Application of interactive genetic algorithm to fashion design. Engineering Applications of Artificial Intelligence, 13(6), 635-644. doi:10.1016/s0952-1976(00)00045-2Krish, S. (2011). A practical generative design method. Computer-Aided Design, 43(1), 88-100. doi:10.1016/j.cad.2010.09.009Lee, H. C., Herawan, T., & Noraziah, A. (2012). Evolutionary grammars based design framework for product innovation. Procedia Technology, 1, 132-136. doi:10.1016/j.protcy.2012.02.026Lim, S., Prats, M., Jowers, I., Chase, S., Garner, S., & McKay, A. (2008). Shape Exploration in Design: Formalising and Supporting a Transformational Process. International Journal of Architectural Computing, 6(4), 415-433. doi:10.1260/147807708787523303Liu, X., Li, Y., Pan, P., & Li, W. (2011). Research on computer-aided creative design platform based on creativity model. Expert Systems with Applications, 38(8), 9973-9990. doi:10.1016/j.eswa.2011.02.032Liu, H., Tang, M., & Frazer, J. H. (2004). Supporting creative design in a visual evolutionary computing environment. Advances in Engineering Software, 35(5), 261-271. doi:10.1016/j.advengsoft.2004.03.006Lo, C.-H., Ko, Y.-C., & Hsiao, S.-W. (2015). A study that applies aesthetic theory and genetic algorithms to product form optimization. Advanced Engineering Informatics, 29(3), 662-679. doi:10.1016/j.aei.2015.06.004Lubart, T. (2005). How can computers be partners in the creative process: Classification and commentary on the Special Issue. International Journal of Human-Computer Studies, 63(4-5), 365-369. doi:10.1016/j.ijhcs.2005.04.002McCormack, J. P., Cagan, J., & Vogel, C. M. (2004). Speaking the Buick language: capturing, understanding, and exploring brand identity with shape grammars. Design Studies, 25(1), 1-29. doi:10.1016/s0142-694x(03)00023-1Mok, P. Y., Xu, J., Wang, X. X., Fan, J. T., Kwok, Y. L., & Xin, J. H. (2013). An IGA-based design support system for realistic and practical fashion designs. Computer-Aided Design, 45(11), 1442-1458. doi:10.1016/j.cad.2013.06.014Olsen, L., Samavati, F. F., Sousa, M. C., & Jorge, J. A. (2009). Sketch-based modeling: A survey. Computers & Graphics, 33(1), 85-103. doi:10.1016/j.cag.2008.09.013O’Neill, M., McDermott, J., Swafford, J. M., Byrne, J., Hemberg, E., Brabazon, A., … Hemberg, M. (2010). Evolutionary design using grammatical evolution and shape grammars: designing a shelter. International Journal of Design Engineering, 3(1), 4. doi:10.1504/ijde.2010.032820Prats, M., Lim, S., Jowers, I., Garner, S. W., & Chase, S. (2009). Transforming shape in design: observations from studies of sketching. Design Studies, 30(5), 503-520. doi:10.1016/j.destud.2009.04.002Pugliese, M. J., & Cagan, J. (2002). Capturing a rebel: modeling the Harley-Davidson brand through a motorcycle shape grammar. Research in Engineering Design, 13(3), 139-156. doi:10.1007/s00163-002-0013-1Rahimian, F. P., & Ibrahim, R. (2011). Impacts of VR 3D sketching on novice designers’ spatial cognition in collaborative conceptual architectural design. Design Studies, 32(3), 255-291. doi:10.1016/j.destud.2010.10.003Renner, G., & Ekárt, A. (2003). Genetic algorithms in computer aided design. Computer-Aided Design, 35(8), 709-726. doi:10.1016/s0010-4485(03)00003-4Robertson, B. F., & Radcliffe, D. F. (2009). Impact of CAD tools on creative problem solving in engineering design. Computer-Aided Design, 41(3), 136-146. doi:10.1016/j.cad.2008.06.007Rodrigues, E., Amaral, A. R., Gaspar, A. R., & Gomes, Á. (2015). An Approach to Urban Quarter Design Using Building Generative Design and Thermal Performance Optimization. Energy Procedia, 78, 2899-2904. doi:10.1016/j.egypro.2015.11.662Ruiz-Montiel, M., Boned, J., Gavilanes, J., Jiménez, E., Mandow, L., & Pérez-de-la-Cruz, J.-L. (2013). Design with shape grammars and reinforcement learning. Advanced Engineering Informatics, 27(2), 230-245. doi:10.1016/j.aei.2012.12.004Schon, D. A., & Wiggins, G. (1992). Kinds of seeing and their functions in designing. Design Studies, 13(2), 135-156. doi:10.1016/0142-694x(92)90268-fSelker, T. (2005). Fostering motivation and creativity for computer users. International Journal of Human-Computer Studies, 63(4-5), 410-421. doi:10.1016/j.ijhcs.2005.04.005Shea, K., Aish, R., & Gourtovaia, M. (2005). Towards integrated performance-driven generative design tools. Automation in Construction, 14(2), 253-264. doi:10.1016/j.autcon.2004.07.002Shieh, M.-D., Li, Y., & Yang, C.-C. (2018). Comparison of multi-objective evolutionary algorithms in hybrid Kansei engineering system for product form design. Advanced Engineering Informatics, 36, 31-42. doi:10.1016/j.aei.2018.02.002Singh, V., & Gu, N. (2012). Towards an integrated generative design framework. Design Studies, 33(2), 185-207. doi:10.1016/j.destud.2011.06.001Stones, C., & Cassidy, T. (2010). Seeing and discovering: how do student designers reinterpret sketches and digital marks during graphic design ideation? Design Studies, 31(5), 439-460. doi:10.1016/j.destud.2010.05.003Sun, J., Frazer, J. H., & Mingxi, T. (2007). Shape optimisation using evolutionary techniques in product design. Computers & Industrial Engineering, 53(2), 200-205. doi:10.1016/j.cie.2007.06.010Sun, X., Gong, D., & Zhang, W. (2012). Interactive genetic algorithms with large population and semi-supervised learning. Applied Soft Computing, 12(9), 3004-3013. doi:10.1016/j.asoc.2012.04.021Suwa, M., & Tversky, B. (1997). What do architects and students perceive in their design sketches? A protocol analysis. Design Studies, 18(4), 385-403. doi:10.1016/s0142-694x(97)00008-2Tovey, M. (1997). Styling and design: intuition and analysis in industrial design. Design Studies, 18(1), 5-31. doi:10.1016/s0142-694x(96)00006-3Tovey, M., Porter, S., & Newman, R. (2003). Sketching, concept development and automotive design. Design Studies, 24(2), 135-153. doi:10.1016/s0142-694x(02)00035-2Troiano, L., & Birtolo, C. (2014). Genetic algorithms supporting generative design of user interfaces: Examples. Information Sciences, 259, 433-451. doi:10.1016/j.ins.2012.01.006Turrin, M., von Buelow, P., & Stouffs, R. (2011). Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms. Advanced Engineering Informatics, 25(4), 656-675. doi:10.1016/j.aei.2011.07.009Van Elsas, P. A., & Vergeest, J. S. M. (1998). New functionality for computer-aided conceptual design: the displacement feature. Design Studies, 19(1), 81-102. doi:10.1016/s0142-694x(97)00016-1Vasconcelos, L. A., & Crilly, N. (2016). Inspiration and fixation: Questions, methods, findings, and challenges. Design Studies, 42, 1-32. doi:10.1016/j.destud.2015.11.001Vuletic, T., Duffy, A., Hay, L., McTeague, C., Pidgeon, L., & Grealy, M. (2018). The challenges in computer supported conceptual engineering design. Computers in Industry, 95, 22-37. doi:10.1016/j.compind.2017.11.003Wang, K., & Nickerson, J. V. (2017). A literature review on individual creativity support systems. Computers in Human Behavior, 74, 139-151. doi:10.1016/j.chb.2017.04.03